Short-term load forecasting(STLF)plays a crucial role in the smart grid.However,it is challenging to capture the long-time dependence and the nonlinear relationship due to the comprehensive fluctuations of the electri...Short-term load forecasting(STLF)plays a crucial role in the smart grid.However,it is challenging to capture the long-time dependence and the nonlinear relationship due to the comprehensive fluctuations of the electrical load.In this paper,an STLF model based on gated recurrent unit and multi-head attention(GRU-MA)is proposed to address the aforementioned problems.The proposed model accommodates the time series and nonlinear relationship of load data through gated recurrent unit(GRU)and exploits multi-head attention(MA)to learn the decisive features and long-term dependencies.Additionally,the proposed model is compared with the support vector regression(SVR)model,the recurrent neural network and multi-head attention(RNN-MA)model,the long short-term memory and multi-head attention(LSTM-MA)model,the GRU model,and the temporal convolutional network(TCN)model using the public dataset of the Global Energy Forecasting Competition 2014(GEFCOM2014).The results demonstrate that the GRU-MA model has the best prediction accuracy.展开更多
基金supported by the National Natural Science Foundation of China(61771258)。
文摘Short-term load forecasting(STLF)plays a crucial role in the smart grid.However,it is challenging to capture the long-time dependence and the nonlinear relationship due to the comprehensive fluctuations of the electrical load.In this paper,an STLF model based on gated recurrent unit and multi-head attention(GRU-MA)is proposed to address the aforementioned problems.The proposed model accommodates the time series and nonlinear relationship of load data through gated recurrent unit(GRU)and exploits multi-head attention(MA)to learn the decisive features and long-term dependencies.Additionally,the proposed model is compared with the support vector regression(SVR)model,the recurrent neural network and multi-head attention(RNN-MA)model,the long short-term memory and multi-head attention(LSTM-MA)model,the GRU model,and the temporal convolutional network(TCN)model using the public dataset of the Global Energy Forecasting Competition 2014(GEFCOM2014).The results demonstrate that the GRU-MA model has the best prediction accuracy.
